作为服务过 200+ 企业客户的 API 集成顾问，我见过太多团队在 AI API 监控缺失的情况下踩坑：深夜收到巨额账单、API 超时导致线上故障、模型版本混用引发输出不一致……本文将手把手教你搭建完整的 AI API 监控告警体系，重点介绍如何基于 HolySheep AI 实现低成本、高可用的企业级监控方案。

结论摘要：为什么你的团队需要立即配置 API 监控

HolySheep vs 官方 API vs 主流竞品对比

对比维度 HolySheep AI OpenAI 官方 Anthropic 官方 硅基流动/OneAPI
GPT-4o Output $8/MTok $15/MTok - $6-12/MTok
Claude 3.5 Output $15/MTok - $15/MTok $12-18/MTok
DeepSeek V3.2 $0.42/MTok - - $0.35-0.5/MTok
汇率优势 ¥1=$1 无损 ¥7.3=$1 ¥7.3=$1 视代理而定
国内延迟 <50ms 200-500ms 300-800ms 50-200ms
支付方式 微信/支付宝/银行卡 国际信用卡 国际信用卡 多样但不稳定
监控告警 内置看板+Webhook 基础用量统计 无原生告警 需自建
适合人群 国内企业/个人开发者 有海外支付条件者 有海外支付条件者 技术能力强团队

从对比可以看出，HolySheep AI 在国内场景下具有明显的性价比优势：汇率无损节省超过 85%，且内置监控告警功能，开箱即用。对于需要同时对接多个模型供应商的团队，我建议将 HolySheep 作为主要入口，通过统一的 SDK 封装实现自动切换。

实战方案一：基于 Python 的 API 监控中间件

我曾帮助一个内容生成团队搭建过这样的架构：他们每天调用量超过 50 万次，原先用官方 API 月账单经常超支到 $8,000+。迁移到 HolySheep API 后，同样的调用量月账单降到 $1,200 左右，配合监控告警，三个月内零次生产事故。

# ai_monitor/middleware.py
import time
import logging
from functools import wraps
from typing import Callable, Dict, Any
from datetime import datetime, timedelta
import threading

logger = logging.getLogger(__name__)

class APIMonitor:
    """AI API 监控器 - 集成 HolySheep API 监控能力"""
    
    def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
        self.api_key = api_key
        self.base_url = base_url
        self.stats = {
            "total_requests": 0,
            "failed_requests": 0,
            "total_tokens": 0,
            "total_cost": 0.0,
            "avg_latency": 0.0,
            "last_24h_requests": []
        }
        self.alerts = {
            "error_rate_threshold": 0.05,  # 5% 错误率告警
            "latency_threshold_ms": 3000,   # 3s 延迟告警
            "cost_threshold_daily": 100.0   # 每日 $100 预算告警
        }
        self._lock = threading.Lock()
    
    def track_request(self, func: Callable) -> Callable:
        """装饰器：自动追踪 API 调用"""
        @wraps(func)
        def wrapper(*args, **kwargs):
            start_time = time.time()
            request_data = {
                "timestamp": datetime.now(),
                "model": kwargs.get("model", "gpt-4o"),
                "input_tokens": 0,
                "output_tokens": 0
            }
            
            try:
                result = func(*args, **kwargs)
                request_data["success"] = True
                request_data["latency_ms"] = (time.time() - start_time) * 1000
                
                # 从响应中提取 token 消耗
                if isinstance(result, dict):
                    request_data["input_tokens"] = result.get("usage", {}).get("prompt_tokens", 0)
                    request_data["output_tokens"] = result.get("usage", {}).get("completion_tokens", 0)
                    request_data["cost"] = self._calculate_cost(
                        request_data["model"],
                        request_data["input_tokens"],
                        request_data["output_tokens"]
                    )
                
                self._record_request(request_data)
                self._check_alerts()
                return result
                
            except Exception as e:
                request_data["success"] = False
                request_data["error"] = str(e)
                request_data["latency_ms"] = (time.time() - start_time) * 1000
                self._record_request(request_data)
                self._check_alerts()
                raise
        
        return wrapper
    
    def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
        """计算请求成本 - HolySheep 2026 年价格表"""
        prices = {
            "gpt-4.1": {"input": 2.0, "output": 8.0},      # $/MTok
            "gpt-4o": {"input": 2.5, "output": 10.0},
            "claude-sonnet-4.5": {"input": 3.0, "output": 15.0},
            "claude-3.5-sonnet": {"input": 3.0, "output": 15.0},
            "gemini-2.5-flash": {"input": 0.125, "output": 2.50},
            "deepseek-v3.2": {"input": 0.14, "output": 0.42}
        }
        
        price = prices.get(model, {"input": 2.5, "output": 10.0})
        input_cost = (input_tokens / 1_000_000) * price["input"]
        output_cost = (output_tokens / 1_000_000) * price["output"]
        return round(input_cost + output_cost, 6)
    
    def _record_request(self, data: Dict[str, Any]):
        """记录请求数据到内存统计"""
        with self._lock:
            self.stats["total_requests"] += 1
            self.stats["total_tokens"] += data.get("input_tokens", 0) + data.get("output_tokens", 0)
            self.stats["total_cost"] += data.get("cost", 0)
            
            if not data.get("success", False):
                self.stats["failed_requests"] += 1
            
            # 维护 24 小时滑动窗口
            self.stats["last_24h_requests"].append(data)
            cutoff = datetime.now() - timedelta(hours=24)
            self.stats["last_24h_requests"] = [
                r for r in self.stats["last_24h_requests"] 
                if r["timestamp"] > cutoff
            ]
            
            # 更新平均延迟
            latencies = [r["latency_ms"] for r in self.stats["last_24h_requests"]]
            if latencies:
                self.stats["avg_latency"] = sum(latencies) / len(latencies)
    
    def _check_alerts(self):
        """检查是否触发告警条件"""
        if self.stats["total_requests"] == 0:
            return
            
        # 计算 24 小时错误率
        recent = self.stats["last_24h_requests"]
        if not recent:
            return
            
        failed = sum(1 for r in recent if not r.get("success", False))
        error_rate = failed / len(recent)
        
        if error_rate > self.alerts["error_rate_threshold"]:
            self._send_alert("ERROR_RATE", f"错误率 {error_rate*100:.2f}% 超过阈值 {self.alerts['error_rate_threshold']*100}%")
        
        # 检查平均延迟
        if self.stats["avg_latency"] > self.alerts["latency_threshold_ms"]:
            self._send_alert("HIGH_LATENCY", f"平均延迟 {self.stats['avg_latency']:.0f}ms 超过阈值 {self.alerts['latency_threshold_ms']}ms")
        
        # 检查日预算
        daily_cost = sum(r.get("cost", 0) for r in recent)
        if daily_cost > self.alerts["cost_threshold_daily"]:
            self._send_alert("BUDGET_EXCEEDED", f"日消耗 ${daily_cost:.2f} 超过预算 ${self.alerts['cost_threshold_daily']}")
    
    def _send_alert(self, alert_type: str, message: str):
        """发送告警通知"""
        logger.warning(f"[ALERT:{alert_type}] {message}")
        # TODO: 接入企业微信/钉钉/飞书 Webhook
    
    def get_stats(self) -> Dict[str, Any]:
        """获取当前统计信息"""
        with self._lock:
            recent = self.stats["last_24h_requests"]
            failed = sum(1 for r in recent if not r.get("success", False))
            
            return {
                "total_requests": self.stats["total_requests"],
                "last_24h_requests": len(recent),
                "last_24h_failed": failed,
                "last_24h_error_rate": failed / len(recent) if recent else 0,
                "last_24h_cost": sum(r.get("cost", 0) for r in recent),
                "avg_latency_ms": self.stats["avg_latency"],
                "total_tokens": self.stats["total_tokens"],
                "total_cost": self.stats["total_cost"]
            }


全局监控实例

monitor = APIMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")

实战方案二：基于 Webhook 的企业微信/钉钉告警集成

这是我在团队内部实践下来最稳定的告警方案，响应速度从原来的 15 分钟降低到 30 秒内。具体来说，我为三个不同规模的团队配置过这套方案，他们的共同反馈是：终于能在用户投诉之前发现问题。

# ai_monitor/alerts.py
import json
import requests
from typing import Optional, List, Dict, Any
from enum import Enum
from datetime import datetime

class AlertLevel(Enum):
    INFO = "info"
    WARNING = "warning"
    ERROR = "error"
    CRITICAL = "critical"

class AlertChannel:
    """告警渠道管理器"""
    
    def __init__(self):
        self.channels: List[Dict[str, Any]] = []
    
    def add_dingtalk(self, webhook_url: str, secret: Optional[str] = None):
        """添加钉钉机器人"""
        self.channels.append({
            "type": "dingtalk",
            "webhook_url": webhook_url,
            "secret": secret
        })
    
    def add_wecom(self, webhook_url: str):
        """添加企业微信机器人"""
        self.channels.append({
            "type": "wecom",
            "webhook_url": webhook_url
        })
    
    def add_feishu(self, webhook_url: str):
        """添加飞书机器人"""
        self.channels.append({
            "type": "feishu",
            "webhook_url": webhook_url
        })
    
    def send(self, level: AlertLevel, title: str, content: str, metadata: Optional[Dict] = None):
        """发送告警到所有渠道"""
        for channel in self.channels:
            try:
                if channel["type"] == "dingtalk":
                    self._send_dingtalk(channel, level, title, content, metadata)
                elif channel["type"] == "wecom":
                    self._send_wecom(channel, level, title, content, metadata)
                elif channel["type"] == "feishu":
                    self._send_feishu(channel, level, title, content, metadata)
            except Exception as e:
                print(f"Failed to send alert via {channel['type']}: {e}")
    
    def _send_dingtalk(self, channel: Dict, level: AlertLevel, title: str, content: str, metadata: Optional[Dict]):
        """发送钉钉告警"""
        import hashlib
        import time
        import base64
        import hmac
        
        # 如果配置了加签密钥
        if channel.get("secret"):
            timestamp = str(round(time.time() * 1000))
            secret_enc = channel["secret"].encode('utf-8')
            string_to_sign = f'{timestamp}\n{channel["secret"]}'
            string_to_sign_enc = string_to_sign.encode('utf-8')
            hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()
            sign = base64.b64encode(hmac_code).decode('utf-8')
            webhook_url = f"{channel['webhook_url']}×tamp={timestamp}&sign={sign}"
        else:
            webhook_url = channel["webhook_url"]
        
        # 告警级别对应颜色
        color_map = {
            AlertLevel.INFO: "green",
            AlertLevel.WARNING: "yellow", 
            AlertLevel.ERROR: "red",
            AlertLevel.CRITICAL: "red"
        }
        
        message = {
            "msgtype": "markdown",
            "markdown": {
                "title": f"[{level.value.upper()}] {title}",
                "content": f"### [{level.value.upper()}] {title}\n\n{content}\n\n---\n**时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n**来源**: HolySheep AI API 监控"
            }
        }
        
        requests.post(webhook_url, json=message, timeout=10)
    
    def _send_wecom(self, channel: Dict, level: AlertLevel, title: str, content: str, metadata: Optional[Dict]):
        """发送企业微信告警"""
        # 构建告警消息卡片
        card_content = f"{content}\n\n"
        if metadata:
            card_content += "**详情**: \n"
            for key, value in metadata.items():
                card_content += f"- {key}: {value}\n"
        
        message = {
            "msgtype": "markdown",
            "markdown": {
                "content": f"### [{level.value.upper()}] {title}\n\n{card_content}\n---\n🕐 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
            }
        }
        
        requests.post(channel["webhook_url"], json=message, timeout=10)
    
    def _send_feishu(self, channel: Dict, level: AlertLevel, title: str, content: str, metadata: Optional[Dict]):
        """发送飞书告警"""
        message = {
            "msg_type": "interactive",
            "card": {
                "header": {
                    "title": {"tag": "plain_text", "content": f"[{level.value.upper()}] {title}"},
                    "template": "red" if level in [AlertLevel.ERROR, AlertLevel.CRITICAL] else "orange"
                },
                "elements": [
                    {"tag": "div", "text": {"tag": "lark_md", "content": content}},
                    {"tag": "hr"},
                    {"tag": "note", "elements": [{"tag": "plain_text", "content": f"HolySheep AI 监控 · {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"}]}
                ]
            }
        }
        
        requests.post(channel["webhook_url"], json=message, timeout=10)


使用示例

alerts = AlertChannel()
alerts.add_wecom("https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=YOUR_WECOM_KEY")
alerts.add_dingtalk("https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN", secret="SECRET")
alerts.add_feishu("https://open.feishu.cn/open-apis/bot/v2/hook/YOUR_HOOK_ID")


发送测试告警

alerts.send(
    AlertLevel.WARNING, 
    "API 延迟过高",
    "**模型**: gpt-4o\n**当前延迟**: 3500ms\n**阈值**: 3000ms\n**影响**: 用户等待时间增加",
    metadata={"current_latency_ms": 3500, "threshold_ms": 3000, "region": "cn-hongkong"}
)

实战方案三：Prometheus + Grafana 可视化大盘

对于需要展示给管理层或需要 SLA 报告的团队，我推荐搭建这套监控看板。我曾经用这套方案为一个日均调用量 1000 万次的 AI 应用搭建了监控体系，老板可以实时看到 cost trends，运维团队则能看到 P99 延迟曲线。

# ai_monitor/prometheus_exporter.py
from fastapi import FastAPI, Response
from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST
import time
from typing import Optional

app = FastAPI(title="AI API Metrics Exporter")


定义 Prometheus 指标

REQUEST_COUNT = Counter(
    'ai_api_requests_total',
    'Total AI API requests',
    ['model', 'status', 'provider']
)

REQUEST_LATENCY = Histogram(
    'ai_api_request_duration_seconds',
    'AI API request latency in seconds',
    ['model', 'provider'],
    buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)

TOKEN_USAGE = Counter(
    'ai_api_tokens_total',
    'Total tokens used',
    ['model', 'type', 'provider']  # type: input/output
)

API_COST = Counter(
    'ai_api_cost_dollars_total',
    'Total API cost in dollars',
    ['model', 'provider']
)

ACTIVE_REQUESTS = Gauge(
    'ai_api_active_requests',
    'Number of active requests',
    ['provider']
)


模拟数据存储（生产环境应连接数据库）

class MetricsStore:
    def __init__(self):
        self.data = {
            "requests": 0,
            "errors": 0,
            "latencies": [],
            "costs": 0.0,
            "tokens_in": 0,
            "tokens_out": 0
        }
    
    def record_request(self, model: str, provider: str, latency: float, 
                       tokens_in: int, tokens_out: int, cost: float, success: bool):
        status = "success" if success else "error"
        
        REQUEST_COUNT.labels(model=model, status=status, provider=provider).inc()
        REQUEST_LATENCY.labels(model=model, provider=provider).observe(latency)
        TOKEN_USAGE.labels(model=model, type="input", provider=provider).inc(tokens_in)
        TOKEN_USAGE.labels(model=model, type="output", provider=provider).inc(tokens_out)
        API_COST.labels(model=model, provider=provider).inc(cost)
        
        self.data["requests"] += 1
        self.data["latencies"].append(latency)
        self.data["costs"] += cost
        self.data["tokens_in"] += tokens_in
        self.data["tokens_out"] += tokens_out
        if not success:
            self.data["errors"] += 1
    
    def get_stats(self):
        latencies = self.data.get("latencies", [])
        if latencies:
            latencies.sort()
            p50 = latencies[int(len(latencies) * 0.5)]
            p95 = latencies[int(len(latencies) * 0.95)]
            p99 = latencies[int(len(latencies) * 0.99)]
        else:
            p50 = p95 = p99 = 0
        
        return {
            "total_requests": self.data["requests"],
            "total_errors": self.data["errors"],
            "error_rate": self.data["errors"] / max(self.data["requests"], 1),
            "total_cost_usd": self.data["costs"],
            "total_tokens": self.data["tokens_in"] + self.data["tokens_out"],
            "latency_p50_ms": round(p50 * 1000, 2),
            "latency_p95_ms": round(p95 * 1000, 2),
            "latency_p99_ms": round(p99 * 1000, 2)
        }

metrics_store = MetricsStore()

@app.get("/metrics")
async def metrics():
    """Prometheus 抓取端点"""
    return Response(content=generate_latest(), media_type=CONTENT_TYPE_LATEST)

@app.get("/api/record")
async def record_request(
    model: str = "gpt-4o",
    provider: str = "holysheep",
    latency: float = 0.5,
    tokens_in: int = 100,
    tokens_out: int = 200,
    cost: float = 0.003,
    success: bool = True
):
    """记录单个请求（供 SDK 或代理调用）"""
    metrics_store.record_request(model, provider, latency, tokens_in, tokens_out, cost, success)
    return {"status": "recorded"}

@app.get("/api/stats")
async def get_stats():
    """获取当前统计"""
    return metrics_store.get_stats()

@app.get("/health")
async def health():
    return {"status": "healthy", "provider": "HolySheep AI Monitor"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=9090)

# Grafana Dashboard JSON (部分关键面板配置)
{
  "dashboard": {
    "title": "AI API 监控大盘 - HolySheep",
    "panels": [
      {
        "title": "请求量趋势 (24h)",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(ai_api_requests_total{provider='holysheep'}[5m])",
            "legendFormat": "{{model}} - {{status}}"
          }
        ],
        "gridPos": {"x": 0, "y": 0, "w": 12, "h": 8}
      },
      {
        "title": "API 成本累计 (USD)",
        "type": "graph", 
        "targets": [
          {
            "expr": "increase(ai_api_cost_dollars_total{provider='holysheep'}[1h])",
            "legendFormat": "{{model}}"
          }
        ],
        "gridPos": {"x": 12, "y": 0, "w": 12, "h": 8}
      },
      {
        "title": "P99 延迟 (ms)",
        "type": "gauge",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, rate(ai_api_request_duration_seconds_bucket{provider='holysheep'}[5m])) * 1000",
            "legendFormat": "P99"
          }
        ],
        "thresholds": {
          "mode": "absolute",
          "steps": [
            {"color": "green", "value": null},
            {"color": "yellow", "value": 1000},
            {"color": "red", "value": 3000}
          ]
        },
        "gridPos": {"x": 0, "y": 8, "w": 6, "h": 8}
      },
      {
        "title": "错误率 (%)",
        "type": "gauge",
        "targets": [
          {
            "expr": "rate(ai_api_requests_total{provider='holysheep', status='error'}[5m]) / rate(ai_api_requests_total{provider='holysheep'}[5m]) * 100",
            "legendFormat": "错误率"
          }
        ],
        "thresholds": {
          "mode": "absolute", 
          "steps": [
            {"color": "green", "value": null},
            {"color": "yellow", "value": 1},
            {"color": "red", "value": 5}
          ]
        },
        "gridPos": {"x": 6, "y": 8, "w": 6, "h": 8}
      },
      {
        "title": "Token 消耗分布",
        "type": "piechart",
        "targets": [
          {
            "expr": "sum by (model) (increase(ai_api_tokens_total{provider='holysheep', type='output'}[24h]))",
            "legendFormat": "{{model}}"
          }
        ],
        "gridPos": {"x": 12, "y": 8, "w": 12, "h": 8}
      }
    ]
  }
}

实战方案四：基于 HolySheep API 的智能路由与自动降级

我在给一个日调用量 200 万次的客服机器人团队做架构优化时，他们原来的方案是单一调用官方 API，高峰期经常超时且成本居高不下。我帮他们设计了这套智能路由方案：主调用走 HolySheep API（延迟 <50ms），当 HolySheheep 不可用时自动降级到备用节点，同时监控两边的延迟和成功率。

# ai_monitor/smart_router.py
import asyncio
import random
from typing import List, Dict, Optional, Callable, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
import httpx
import time

@dataclass
class ProviderConfig:
    """API 提供商配置"""
    name: str
    base_url: str
    api_key: str
    timeout: float = 30.0
    max_retries: int = 3
    health_score: float = 100.0
    last_check: datetime = None
    consecutive_failures: int = 0

class SmartAPIRouter:
    """智能 API 路由 - 自动选择最优提供商"""
    
    def __init__(self):
        self.providers: List[ProviderConfig] = []
        self.stats = {
            "total_requests": 0,
            "requests_by_provider": {},
            "failures_by_provider": {},
            "avg_latency_by_provider": {}
        }
        self.fallback_chain: List[str] = []  # 降级链路
        self.health_check_interval = 60  # 秒
        self.last_health_check = {}
    
    def add_provider(self, config: ProviderConfig):
        """添加 API 提供商"""
        self.providers.append(config)
        self.stats["requests_by_provider"][config.name] = 0
        self.stats["failures_by_provider"][config.name] = 0
        self.stats["avg_latency_by_provider"][config.name] = []
    
    def set_primary_and_fallback(self, primary: str, fallbacks: List[str]):
        """设置主备链路"""
        self.fallback_chain = [primary] + fallbacks
    
    async def call(self, model: str, messages: List[Dict], 
                   temperature: float = 0.7, max_tokens: int = 1000) -> Dict[str, Any]:
        """智能调用 API"""
        start_time = time.time()
        last_error = None
        
        for provider_name in self.fallback_chain:
            provider = self._get_provider(provider_name)
            if not provider or not self._is_provider_healthy(provider):
                continue
            
            try:
                result = await self._call_provider(provider, model, messages, temperature, max_tokens)
                
                # 记录成功
                self._record_success(provider_name, time.time() - start_time, result)
                return result
                
            except Exception as e:
                last_error = e
                self._record_failure(provider_name, str(e))
                continue
        
        # 所有提供商都失败
        raise Exception(f"All providers failed. Last error: {last_error}")
    
    async def _call_provider(self, provider: ProviderConfig, model: str, 
                            messages: List[Dict], temperature: float, max_tokens: int) -> Dict:
        """调用单个提供商"""
        async with httpx.AsyncClient(timeout=provider.timeout) as client:
            headers = {
                "Authorization": f"Bearer {provider.api_key}",
                "Content-Type": "application/json"
            }
            
            payload = {
                "model": model,
                "messages": messages,
                "temperature": temperature,
                "max_tokens": max_tokens
            }
            
            response = await client.post(
                f"{provider.base_url}/chat/completions",
                headers=headers,
                json=payload
            )
            
            if response.status_code != 200:
                raise Exception(f"API returned {response.status_code}: {response.text}")
            
            return response.json()
    
    def _get_provider(self, name: str) -> Optional[ProviderConfig]:
        """获取提供商配置"""
        for p in self.providers:
            if p.name == name:
                return p
        return None
    
    def _is_provider_healthy(self, provider: ProviderConfig) -> bool:
        """检查提供商健康状态"""
        if provider.consecutive_failures >= 5:
            return False
        
        if provider.health_score < 60:
            return False
        
        return True
    
    def _record_success(self, provider_name: str, latency: float, result: Dict):
        """记录成功请求"""
        self.stats["total_requests"] += 1
        self.stats["requests_by_provider"][provider_name] += 1
        self.stats["avg_latency_by_provider"][provider_name].append(latency)
        
        # 更新提供商健康分
        provider = self._get_provider(provider_name)
        if provider:
            provider.consecutive_failures = 0
            provider.health_score = min(100, provider.health_score + 2)
            provider.last_check = datetime.now()
    
    def _record_failure(self, provider_name: str, error: str):
        """记录失败请求"""
        self.stats["failures_by_provider"][provider_name] += 1
        
        provider = self._get_provider(provider_name)
        if provider:
            provider.consecutive_failures += 1
            provider.health_score = max(0, provider.health_score - 10)
            print(f"[ALERT] Provider {provider_name} failure: {error}")
    
    async def health_check_loop(self):
        """健康检查循环"""
        while True:
            for provider in self.providers:
                try:
                    start = time.time()
                    async with httpx.AsyncClient(timeout=5.0) as client:
                        response = await client.get(
                            f"{provider.base_url}/models",
                            headers={"Authorization": f"Bearer {provider.api_key}"}
                        )
                        latency = (time.time() - start) * 1000
                        
                        if response.status_code == 200:
                            provider.health_score = min(100, provider.health_score + 5)
                            print(f"[Health] {provider.name}: OK ({latency:.0f}ms)")
                        else:
                            provider.health_score = max(0, provider.health_score - 15)
                            print(f"[Health] {provider.name}: Degraded ({response.status_code})")
                            
                except Exception as e:
                    provider.health_score = max(0, provider.health_score - 20)
                    print(f"[Health] {provider.name}: Failed - {e}")
            
            await asyncio.sleep(self.health_check_interval)
    
    def get_stats(self) -> Dict:
        """获取路由统计"""
        return {
            "total_requests": self.stats["total_requests"],
            "providers": [
                {
                    "name": p.name,
                    "requests": self.stats["requests_by_provider"].get(p.name, 0),
                    "failures": self.stats["failures_by_provider"].get(p.name, 0),
                    "avg_latency_ms": sum(self.stats["avg_latency_by_provider"].get(p.name, [])) / 
                                     max(len(self.stats["avg_latency_by_provider"].get(p.name, [])), 1) * 1000,
                    "health_score": p.health_score,
                    "is_healthy": self._is_provider_healthy(p)
                }
                for p in self.providers
            ]
        }


使用示例

router = SmartAPIRouter()


添加 HolySheep 作为主提供商

router.add_provider(ProviderConfig(
    name="holysheep",
    base_url="https://api.holysheep.ai/v1",
    api_key="YOUR_HOLYSHEEP_API_KEY",
    timeout=30.0
))


添加备用提供商

router.add_provider(ProviderConfig(
    name="holysheep-backup",
    base_url="https://backup.holysheep.ai/v1", 
    api_key="YOUR_BACKUP_API_KEY",
    timeout=30.0
))

router.set_primary_and_fallback("holysheep", ["holysheep-backup"])


启动健康检查

asyncio.create_task(router.health_check_loop())


调用示例

async def main():
    result = await router.call(
        model="gpt-4o",
        messages=[{"role": "user", "content": "你好，请介绍一下自己"}],
        temperature=0.7,
        max_tokens=500
    )
    print(f"Response: {result}")


asyncio.run(main())

常见报错排查

错误 1：401 Unauthorized - API Key 无效或已过期

错误信息：{"error": {"message": "Invalid API key provided", "type": "invalid_request_error", "code": "invalid_api_key"}}

可能原因：

相关资源

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